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EP2626005A1 - Procédé de détermination de l'état physique et/ou psychique d'un sujet à l'aide d'une analyse de la variation des taux cardiaques - Google Patents

Procédé de détermination de l'état physique et/ou psychique d'un sujet à l'aide d'une analyse de la variation des taux cardiaques Download PDF

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Publication number
EP2626005A1
EP2626005A1 EP12154764.0A EP12154764A EP2626005A1 EP 2626005 A1 EP2626005 A1 EP 2626005A1 EP 12154764 A EP12154764 A EP 12154764A EP 2626005 A1 EP2626005 A1 EP 2626005A1
Authority
EP
European Patent Office
Prior art keywords
analysis
heart rate
frequency distribution
intervals
rate variability
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP12154764.0A
Other languages
German (de)
English (en)
Inventor
Gmbh Pulse7
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Pulse GmbH and Co KG
Original Assignee
Pulse GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Pulse GmbH filed Critical Pulse GmbH
Priority to EP12154764.0A priority Critical patent/EP2626005A1/fr
Priority to EP13706447.3A priority patent/EP2811898B1/fr
Priority to US14/377,248 priority patent/US9848784B2/en
Priority to CA2863928A priority patent/CA2863928A1/fr
Priority to PCT/EP2013/052573 priority patent/WO2013117710A1/fr
Priority to KR1020147023800A priority patent/KR20140123082A/ko
Priority to AU2013217991A priority patent/AU2013217991B2/en
Publication of EP2626005A1 publication Critical patent/EP2626005A1/fr
Withdrawn legal-status Critical Current

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02405—Determining heart rate variability
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • A61B5/352—Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4029—Detecting, measuring or recording for evaluating the nervous system for evaluating the peripheral nervous systems
    • A61B5/4035—Evaluating the autonomic nervous system
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4884—Other medical applications inducing physiological or psychological stress, e.g. applications for stress testing
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7246—Details of waveform analysis using correlation, e.g. template matching or determination of similarity
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7282—Event detection, e.g. detecting unique waveforms indicative of a medical condition

Definitions

  • the invention relates to a method for determining the physical and / or mental state of a subject on the basis of an analysis of his heart rate variability (HRV), in which analysis at least one frequency distribution of interbeat intervals (IBI) recorded in at least one examination period is examined.
  • HRV his heart rate variability
  • IBI interbeat intervals
  • RR intervals RR intervals
  • heart rate variability by quantifying the fluctuations measured thereby, in order to determine the influence of the autonomic nervous system on cardiac activity and subsequently draw conclusions about the mental and physical condition.
  • two persons having the same heart rhythm, in their regulatory state by the autonomic nervous system and thus in their health and performance can differ significantly.
  • such methods attempt to represent the frequency of results of an IBI measurement in the form of a Gaussian curve. From the position of this maximum or the Gaussian curve, the distribution of the classes surrounding this maximum, or the standard deviation and / or symmetry of the Gaussian curve, etc.
  • the invention has therefore set itself the task of developing a method by means of which the physical and / or mental state of a subject can be concluded simply, quickly and reproducibly.
  • the inventive method should be largely independent of parameters such as ambient temperature, time of day, etc. can be applied.
  • the invention achieves the object in that the analysis of heart rate variability (HRV) comprises at least one examination of at least one frequency distribution for a multimodal distribution.
  • HRV heart rate variability
  • HRV heart rate variability
  • HRV heart rate variability
  • an indication of a particular may be a dysregulation of the heart by the autonomic nervous system, even if two modal values can be identified with very little difference in the lengths of their interbeat intervals (IBI).
  • IBI interbeat intervals
  • More than one mode value can be interpreted as a kind of deviation from a fundamental frequency. It may therefore mean that the cardiac activity is determined not only by a variability of the IBI lengths around a so-called fundamental oscillation, which fundamental oscillation through a certain bandwidth of IBI lengths by a maximum, which is characterized by a particularly large number of measured interbeat interval lengths , Rather, the heart activity thus forms several maxima at frequencies of interbeat intervals, which are influenced by the autonomic nervous system with respect to their lengths.
  • RR intervals of the heart and / or intervals between pulse waves are used as interbeat intervals, a particularly accurate or simple determination of the heart rate variability can take place.
  • RR interval denotes the distance between two R waves of an electrocardiogram, but of course points and / or leads of an ECG may also be suitable for the method according to the invention.
  • the distances between pulse waves are used, a particularly simple, yet accurate determination can be made possible.
  • a determination of inter-beat intervals using known corresponding devices - may be mentioned by way of example be ear clip or finger sensor, which measure about the change in the light transmission of the skin with infrared or hand pulse sensors.
  • a meaningful analysis of the heart rate variability is possible in a quick and simple way.
  • a histogram is mentioned in which the lengths of the IBI recorded during an investigation period are recorded according to their frequency.
  • a classified frequency distribution is used, a simplified representation, analysis and / or processing of the obtained measured values can take place.
  • the class boundaries or the class widths are set in such a way that any mode values do not overlap, overlay, etc. and otherwise adversely affect the method according to the invention.
  • it may also play a role, which length of the investigation period is selected for the measurement of interbeat intervals.
  • a long-term measurement of 20 minutes and more, as is common in the art, may clearly favor overlaying of possible modes, or at least greatly complicate the analysis.
  • the method according to the invention thus advantageously takes into account both the number of identified maxima and a variability of the IBI around these maxima. In order to identify such modes, various methods are known, including threshold value analyzes.
  • the frequency distribution analysis is carried out on a bivariate statistical analysis, a more precise analysis of the HRV can result, among other things, because inter-beat intervals can be shown. If more than one expression is used for the analysis, an improved inference can be made to the heart activity.
  • At least one interbeat interval with its subsequent interbeat interval is used for the bivariate statistical analysis, a particularly accurate analysis of the HRV can be made possible, since, inter alia, it can be seen how erratic the lengths of successive inter-beat intervals to change. Strong fluctuations in the lengths of successive interbeat intervals could therefore suggest that the heart's regulatory state is in a relatively unbalanced state, which in turn may allow conclusions to be drawn about the physical and / or mental state of a subject. If interbeat intervals, whose respective subsequent IBI differs significantly in their length, even form one or more modes, it is thus possible to assume a comparatively disadvantageous regulatory state of the cardiac activity.
  • the bivariate statistical analysis includes a classification of the frequency distribution as a function of the differences between two consecutive interbeat interval lengths, a meaningful and above all easily interpretable information about the HRV or the activity of the vegetative nervous system of a subject can be obtained. Again, based on such an analysis, improved conclusions can be made about the mental and / or physical condition of a subject, on the basis of which, for example, health status, ability to regenerate, ideal intensity of a physical training session and so on can be assessed in an improved manner.
  • the informative value of the method according to the invention can be further improved.
  • a large difference between two possibly determined modal values with respect to the lengths of the IBI intervals can, according to the above version, suggest the conclusion of a comparatively disadvantageous heart rate variability - the more disadvantageous, the larger this difference determined appears.
  • analysis of heart rate variability may consider a ratio of the densities of the modal distributions, particularly the integrals of these densities.
  • ratios of the modes relative to each other or also the proportions of the RR intervals associated with the respective modes can be represented or evaluated.
  • This may also be suitable for scatterplots with successive interbeat intervals, wherein the duration of each interbeat interval of a specific measurement period is entered as a function of the duration of the subsequent interbeat interval.
  • a graphic representation could yield a two-dimensional image in which the density of the modal distribution is marked in color or by contour lines - but of course other representations of the method according to the invention would also be conceivable, for example a three-dimensional one. In this way, a much faster or simplified analysis of the HRV can be made possible, and in particular also taking into account the occurrence of multiple frequencies, or their relationships, relation and / or dependence on each other.
  • the analysis according to the invention can be further improved if, in the determination of the physical and / or mental state of a subject, the analysis data of the heart rate variability (HRV) with comparison data are related to other subjects.
  • HRV heart rate variability
  • the mode values of the frequency distribution are determined using a threshold value, it may be possible, for example, to make it easier to classify a multimodal distribution and / or to render it particularly clear. This can also have the advantage that, not least due to a usual in the art measurement duration of 20 minutes and more, as supposed statistical outlier appearing measurement results can also be taken into account more easily.
  • the method according to the invention can be particularly revealing if the frequency distributions of interbeat intervals recorded in at least two examination periods are examined, with different physical and / or psychological stress states acting on the subjects in the examination periods.
  • the adaptability of a subject to different loads can be determined.
  • it can also be simplified to determine which exercise intensity - for example for physical training - best meets the requirements or the condition of the test person.
  • the effect of a possible relaxation training, a change of lifestyle can be quickly and easily determined and / or optimized. It is only necessary to check changes in the heart rate variability and, in particular, any possibly formed maxima at regular intervals.
  • a reduction in the gap between determined modal values can be regarded positively as a positive development.
  • a comparatively short measuring time of the heart activity in the range of a few minutes can be sufficient for a meaningful analysis.
  • a correction value for a time domain analysis of the heart rate variability for example for parameters RMSSD and / or pNN50, can be calculated and taken into account with the aid of the examination of the frequency distribution.
  • Fig. 1 By way of example, the RR intervals of consecutive heartbeats of a test person recorded in an examination period 1 are represented graphically.
  • the time lengths 2 of the RR intervals occurring in seconds [s] along the x-axis of the examination period 1 are plotted in milliseconds [ms] and successive RR intervals are connected to one another by a straight line.
  • the illustration may vary Fig. 1 so the minimum and maximum RR intervals 3 and 4 of a certain period are taken.
  • Another representation, for example as RR tachogram is also conceivable, but not shown.
  • Fig. 2 represents a univariate statistical analysis in the form of a histogram 5, in which, for example, all RR intervals determined in a certain period of time are combined in 50 ms classes where x-axis 6 denotes the length of RR intervals in ms. The heights of these classes shown as bars are given in this histogram in accordance with the scaling of the y-axis 7 and the number of RR intervals associated with each class. In this example, a mode 8 can be seen. From the number and height of the classes, the fluctuation range of the length of the RR intervals can be derived-for example, also by evaluation compared to comparison values.
  • Such a histogram can still be processed, for example to take into account the ratio of the densities of the modal distributions, in particular the integrals of these densities, which is shown in FIG Fig. 2 is not shown in detail. If the frequency distribution shows only one mode and a substantially normal, symmetric density distribution-similar to a Gaussian curve-then, according to the prior art, a comparatively good variability of the heart rates and a good condition of a subject are discussed.
  • the information content of the values may decrease Fig. 1 in a known manner as a scatter plot (scatter plot) according to the Fig. 3 be reproduced.
  • a bivariate statistical analysis may for example be based on two consecutive RR intervals.
  • the duration of each heartbeat 10 is entered in ms on the x-axis as a function of the duration of the heartbeat following this heartbeat in ms on the y-axis 11. It is spoken of an advantageous regulation width, that is, of a comparatively good heart rate variability, if the points shown in this diagram result in a substantially elliptical point cloud, the main axis of this point cloud being substantially above the drawn diagonal 12. Points lying on this diagonal denote two consecutive RR intervals whose length is equal.
  • analysis of heart rate variability now comprises at least one examination of at least one frequency distribution for a multimodal distribution.
  • HRV heart rate variability
  • FIG. 5 is a multimodal distribution recognizable by the formation of two maxima 13, 14, each forming a mode.
  • histograms after the Figures 2 and 5 are to be understood as exemplary representations and of course from this divergent divisions of the classes, scales and units of the x- or y-axis, etc., can or must be made to allow implementation of the method according to the invention.
  • the spacing of 15 determined maxima to each other can be used as a measure.
  • the greater the distance between two modes the more disadvantageous a psychic and / or physical state of a subject is. It may also be necessary to take into account the difference in the heights of two or more determined maxima or modes relative to each other or may also provide a computation of the density distribution essential information on this. From the example of FIG. 5 With a frequency distribution about two maxima 13, 14, it can namely be deduced, inter alia, that the mean heart rate could essentially be formed by two fundamental vibrations, as already explained.
  • Fig. 6 again shows a scatter plot 16, from which it can be seen that an analysis of a frequency distribution can be comparatively difficult, since the values entered in the scatter plot appear in the manner of a shotgun. It can be seen that the lengths of two successive heartbeats are substantially different - one than generally desirable valid ellipsoidal frequency distribution, as this suggestively in Fig. 3 can be seen, is difficult to see. It can also be seen that comparatively many RR interval pairs are relatively far apart from the diagonal 12, which may allow the inference to be drawn that the heart activity appears to be rhythmic or inharmonious, ie deviating from a fundamental vibration.
  • modal values are displayed as a density distribution and their characteristics are marked in the form of contour lines 17.
  • a fast and insightful information on existing modes 18 with respect to the duration of two consecutive RR intervals and / or the ratio of your RR interval time each other can be made possible.
  • Even possible values, which are only evaluated as outliers 19, can thus easily be used for an analysis or incorporated into it.
  • Fig. 18 shows a mode that may generally be considered to be relatively disadvantageous, as it identifies successive RR interval lengths that are relatively different from each other, or that may indicate erratic changes in cardiac rhythm. It is noted that it is also conceivable, three-dimensional Create scatter plots plotting each heartbeat interval as a function of its two subsequent heartbeat intervals on the y and z axes.
  • FIG. 7 also shows a frequency distribution of the RR interval measurement as a histogram 20, which can be understood in the manner of a projection of the scattergram 16.
  • the bivariate statistical analysis comprises a classification of the frequency distribution as a function of the differences between two consecutive interbeat interval lengths.
  • this histogram 20 may allow a study to be made of a multimodal distribution of RR intervals that differ from their subsequent RR intervals in terms of their length.
  • those successive RR interval pairs whose lengths are equal, that is, which lie on the diagonal 12 of the associated scattergram are the number 0 of the X-axis 21 of the histogram in FIG FIG. 7 assigned.
  • This X-axis 21 indicates the amount of difference between two consecutive RR interval lengths.
  • the y-axis 22 of the histogram in turn indicates the frequency of the RR interval pairs. Also in the histogram 20 of the FIG. 7
  • a study will be conducted on a multimodal distribution, which may, for example, provide information on which modes essentially constitute the rhythm of the heart over the respective examination period and / or how rapidly successive RR intervals differ or change their length.
  • a deviation from a, essentially the average heart rate determining fundamental vibration can be assumed.

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EP12154764.0A 2012-02-08 2012-02-09 Procédé de détermination de l'état physique et/ou psychique d'un sujet à l'aide d'une analyse de la variation des taux cardiaques Withdrawn EP2626005A1 (fr)

Priority Applications (7)

Application Number Priority Date Filing Date Title
EP12154764.0A EP2626005A1 (fr) 2012-02-08 2012-02-09 Procédé de détermination de l'état physique et/ou psychique d'un sujet à l'aide d'une analyse de la variation des taux cardiaques
EP13706447.3A EP2811898B1 (fr) 2012-02-08 2013-02-08 Procédé de détermination de l'état physique et/ou psychique d'un sujet à l'aide d'une analyse de la variation des taux cardiaques
US14/377,248 US9848784B2 (en) 2012-02-08 2013-02-08 Method for determining the physical and/or psychological state of a subject
CA2863928A CA2863928A1 (fr) 2012-02-08 2013-02-08 Procede pour determiner l'etat physique et/ou psychique d'un sujet d'experience
PCT/EP2013/052573 WO2013117710A1 (fr) 2012-02-08 2013-02-08 Procédé pour déterminer l'état physique et/ou psychique d'un sujet d'expérience
KR1020147023800A KR20140123082A (ko) 2012-02-08 2013-02-08 피험자의 신체 및/또는 심리 상태를 결정하기 위한 방법
AU2013217991A AU2013217991B2 (en) 2012-02-08 2013-02-08 Method for determining the physical and/or psychological state of a subject

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
EP12154578 2012-02-08
EP12154764.0A EP2626005A1 (fr) 2012-02-08 2012-02-09 Procédé de détermination de l'état physique et/ou psychique d'un sujet à l'aide d'une analyse de la variation des taux cardiaques

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EP12154764.0A Withdrawn EP2626005A1 (fr) 2012-02-08 2012-02-09 Procédé de détermination de l'état physique et/ou psychique d'un sujet à l'aide d'une analyse de la variation des taux cardiaques
EP13706447.3A Not-in-force EP2811898B1 (fr) 2012-02-08 2013-02-08 Procédé de détermination de l'état physique et/ou psychique d'un sujet à l'aide d'une analyse de la variation des taux cardiaques

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EP13706447.3A Not-in-force EP2811898B1 (fr) 2012-02-08 2013-02-08 Procédé de détermination de l'état physique et/ou psychique d'un sujet à l'aide d'une analyse de la variation des taux cardiaques

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US (1) US9848784B2 (fr)
EP (2) EP2626005A1 (fr)
KR (1) KR20140123082A (fr)
AU (1) AU2013217991B2 (fr)
CA (1) CA2863928A1 (fr)
WO (1) WO2013117710A1 (fr)

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CN108814591A (zh) * 2018-03-23 2018-11-16 南京大学 一种心电qrs波群宽度的检测方法及其心电分析方法
CN108814590A (zh) * 2018-03-23 2018-11-16 江苏华康信息技术有限公司 一种心电qrs波群的检测方法及其心电分析方法
CN109222964A (zh) * 2018-07-20 2019-01-18 广州视源电子科技股份有限公司 房颤检测装置及存储介质

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AU2016278356A1 (en) 2015-06-15 2018-01-04 Medibio Limited Method and system for assessing mental state
CA2988419A1 (fr) 2015-06-15 2016-12-22 Medibio Limited Procede et systeme de surveillance d'etats de stress
US10022057B1 (en) * 2015-06-19 2018-07-17 Michael Blake Wearable physiological monitoring and notification system based on real-time heart rate variability analysis
US10231673B2 (en) * 2015-07-16 2019-03-19 Samsung Electronics Company, Ltd. Stress detection based on sympathovagal balance
CN109922726A (zh) * 2016-09-20 2019-06-21 夏普株式会社 状态取得计算机、状态取得方法以及信息处理系统
EP3580921A1 (fr) * 2017-02-09 2019-12-18 Sony Mobile Communications Inc. Système et procédé de commande de notifications dans un dispositif électronique en fonction de l'état de l'utilisateur
EP3506314B1 (fr) * 2017-12-29 2020-09-16 Tata Consultancy Services Limited Systèmes et procédés de detection d'anomalie dans un signal cardiovasculaire utilisant des extrêmes et des répétitions hiérarchiques
CN110236573B (zh) * 2019-06-24 2022-05-20 深圳数联天下智能科技有限公司 心理压力状态的检测方法及相关装置
US20210275110A1 (en) 2019-12-30 2021-09-09 RubyElf, LLC Systems For Synchronizing Different Devices To A Cardiac Cycle And For Generating Pulse Waveforms From Synchronized ECG and PPG Systems

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US20090005696A1 (en) * 2007-06-29 2009-01-01 Intelwave Llc Method for quantitative assessment of the autonomic nervous system based on heart rate variability analysis
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108814591A (zh) * 2018-03-23 2018-11-16 南京大学 一种心电qrs波群宽度的检测方法及其心电分析方法
CN108814590A (zh) * 2018-03-23 2018-11-16 江苏华康信息技术有限公司 一种心电qrs波群的检测方法及其心电分析方法
CN108814591B (zh) * 2018-03-23 2020-12-15 南京大学 一种心电qrs波群宽度的检测方法及其心电分析方法
CN108814590B (zh) * 2018-03-23 2021-01-12 江苏华康信息技术有限公司 一种心电qrs波群的检测方法及其心电分析方法
CN109222964A (zh) * 2018-07-20 2019-01-18 广州视源电子科技股份有限公司 房颤检测装置及存储介质
CN109222964B (zh) * 2018-07-20 2021-02-09 广州视源电子科技股份有限公司 房颤检测装置及存储介质

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AU2013217991B2 (en) 2017-09-07
US20150282722A1 (en) 2015-10-08
US9848784B2 (en) 2017-12-26
EP2811898A1 (fr) 2014-12-17
EP2811898B1 (fr) 2017-06-14
AU2013217991A1 (en) 2014-09-11
WO2013117710A1 (fr) 2013-08-15
CA2863928A1 (fr) 2013-08-15

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